Fully support ExtractDiag in numba

pytensor/link/numba/dispatch/tensor_basic.py

@@ -150,14 +150,44 @@ def numba_funcify_Split(op, **kwargs):
 
 
 @numba_funcify.register(ExtractDiag)
-def numba_funcify_ExtractDiag(op, **kwargs):
-    offset = op.offset
-    # axis1 = op.axis1
-    # axis2 = op.axis2
+def numba_funcify_ExtractDiag(op, node, **kwargs):
+    view = op.view
+    axis1, axis2, offset = op.axis1, op.axis2, op.offset
+
+    if node.inputs[0].type.ndim == 2:
+
+        @numba_basic.numba_njit(inline="always")
+        def extract_diag(x):
+            out = np.diag(x, k=offset)
+
+            if not view:
+                out = out.copy()
+
+            return out
+
+    else:
+        axis1p1 = axis1 + 1
+        axis2p1 = axis2 + 1
+        leading_dims = (slice(None),) * axis1
+        middle_dims = (slice(None),) * (axis2 - axis1 - 1)
 
         @numba_basic.numba_njit(inline="always")
         def extract_diag(x):
-        return np.diag(x, k=offset)
+            if offset >= 0:
+                diag_len = min(x.shape[axis1], max(0, x.shape[axis2] - offset))
+            else:
+                diag_len = min(x.shape[axis2], max(0, x.shape[axis1] + offset))
+            base_shape = x.shape[:axis1] + x.shape[axis1p1:axis2] + x.shape[axis2p1:]
+            out_shape = base_shape + (diag_len,)
+            out = np.empty(out_shape)
+
+            for i in range(diag_len):
+                if offset >= 0:
+                    new_entry = x[leading_dims + (i,) + middle_dims + (i + offset,)]
+                else:
+                    new_entry = x[leading_dims + (i - offset,) + middle_dims + (i,)]
+                out[..., i] = new_entry
+            return out
 
     return extract_diag
 

tests/link/numba/test_tensor_basic.py

@@ -17,6 +17,10 @@ from tests.link.numba.test_basic import (
 )
 
 
+pytest.importorskip("numba")
+from pytensor.link.numba.dispatch import numba_funcify
+
+
 rng = np.random.default_rng(42849)
 
 
@@ -366,6 +370,12 @@ def test_Split_view():
             ),
             0,
         ),
+        (
+            set_test_value(
+                at.matrix(), np.arange(10 * 10, dtype=config.floatX).reshape((10, 10))
+            ),
+            -1,
+        ),
         (
             set_test_value(at.vector(), np.arange(10, dtype=config.floatX)),
             0,
@@ -386,6 +396,23 @@ def test_ExtractDiag(val, offset):
     )
 
 
+@pytest.mark.parametrize("k", range(-5, 4))
+@pytest.mark.parametrize(
+    "axis1, axis2", ((0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3))
+)
+@pytest.mark.parametrize("reverse_axis", (False, True))
+def test_ExtractDiag_exhaustive(k, axis1, axis2, reverse_axis):
+    if reverse_axis:
+        axis1, axis2 = axis2, axis1
+
+    x = at.tensor4("x")
+    x_shape = (2, 3, 4, 5)
+    x_test = np.arange(np.prod(x_shape)).reshape(x_shape)
+    out = at.diagonal(x, k, axis1, axis2)
+    numba_fn = numba_funcify(out.owner.op, out.owner)
+    np.testing.assert_allclose(numba_fn(x_test), np.diagonal(x_test, k, axis1, axis2))
+
+
 @pytest.mark.parametrize(
     "n, m, k, dtype",
     [